Method and apparatus of a gesture based biometric system

ABSTRACT

An apparatus and method for detecting a finger are provided. The method includes capturing an image of a finger, generating a likelihood image of the finger from the captured image, localizing the finger within the likelihood image, determining a boundary of the finger, determining a location of one or more creases of the finger, and comparing the determined location of the one or more creases with crease locations of a finger image stored in a database.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 U.S.C. §119(e) of a U.S.Provisional application filed on Aug. 24, 2010 in the U.S. Patent andTrademark Office and assigned Ser. No. 61/376,563, the entire disclosureof which is hereby incorporated by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to an apparatus and method for providing abiometric input for a portable terminal. More particularly, the presentinvention relates to an apparatus and method for recognizing a gestureas an input for a portable terminal.

2. Description of the Related Art

Portable terminals are becoming increasingly popular based on theadditional and advanced features they provide in conjunction with theirmobility. As advances continue to be made regarding features provided bythe portable terminal, user interface design trends indicate thatintuitive interface and simple interactions are also becomingincreasingly popular. For example, in some instances a simple slideinterface on a touch screen has replaced the traditional touch and pressinterface for unlocking a phone. As part of the development of intuitiveinterfaces, biometric recognition is being explored as a fast andconvenient means to augment current user authentication and userinteraction mechanisms. Biometric recognition or biometrics is a fieldof study that allows for recognition of an individual based on theirbiological and/or behavioral traits such as a fingerprint, a face, ahand, an iris, etc.

User interaction with a portable terminal can typically be categorizedin two basic modes—user authentication and user input. Userauthentication has two basic purposes. The first is to validate thepresence of the user and the second is to ensure that the user has theauthority to use the phone. The first purpose, to validate the presenceof the user, is to avoid an unintended execution of a function, such asavoiding the dialing of a number due to a random and unintendedinteraction of the portable terminal with its environment. This basicfunctionality is currently being provided by requiring the user to presstwo or more specific keys in order to unlock the phone. The secondpurpose, which is increasingly becoming more important with thediversification of the portable terminal as a lifestyle device, isensuring device security. To discourage an unauthorized user, the userof the portable terminal is required to input a password to unlock thephone. User interaction is currently limited to key presses or a mousepointer on a touch screen. However, on such a touch screen, there arebasic limitations that arise from the small size of the device. Further,such an interface cannot be easily used in applications requiring3-Dimensional (3D) scene navigation.

Use of a camera has been proposed for augmenting the user interface. Forexample, the prior art discloses a user interface in which the userswipes his/her finger across the camera and the direction of the swipedetermines the response input to the device. As an example, a cursor maybe moved in a direction corresponding to the swipe. The system of theprior art has two embodiments. In the first embodiment, the user mustswipe his/her finger on the camera while the finger is in contact withthe camera. A limitation of this implementation is that it isinconvenient for the user to locate the camera, especially if it is onthe back side of the cell phone. In the second embodiment, the user cansway his/her finger in front of the camera in order to achieve the sameeffect. However, the system may not specifically recognize the finger,especially if there is a significant movement in the background, whichmay lead to a false positive.

A natural extension of this system is to recognize the user's fingerusing techniques available in the biometric recognition literature. Thatis, the prior art provides an exhaustive review of available biometricrecognition techniques. However, a limitation of the biometricrecognition techniques is that the sensors used to capture biometricfeatures are specialized to the application in order to acquire highquality captures and thus are very expensive as compared to the genericcomponents used in a portable terminal. That is, known methods ofextracting biometric information from a user require using a specializedsensor. However, because the price of these special sensors is so high,their use is effectively prohibited in a portable terminal. Accordingly,there is a need for an apparatus and method that provides for biometricrecognition while maintaining costs at a reasonable level. Moreover,there is a need for an apparatus and method that provides a biometricsensor using a camera of a portable terminal. Given that cameras inportable terminals are ubiquitous, incorporating the proposed biometricsensing will add value without adding complexity.

SUMMARY OF THE INVENTION

Aspects of the present invention are to address at least theabove-mentioned problems and/or disadvantages and to provide at leastthe advantages described below. Accordingly, an aspect of the presentinvention is to provide an apparatus and method for providing abiometric input for a portable terminal.

Another aspect of the present invention is to provide an apparatus andmethod for recognizing a gesture as an input for a portable terminal.

Still another aspect of the present invention is to provide an apparatusand method for recognizing a gesture using currently available imagingsensors of a portable terminal.

In accordance with an aspect of the present invention, a method of aportable terminal for detecting a finger is provided. The methodincludes capturing an image of a finger, generating a likelihood imageof the finger from the captured image, localizing the finger within thelikelihood image, determining a boundary of the finger, determining alocation of one or more creases of the finger, and comparing thedetermined location of the one or more creases with crease locations ofa finger image stored in a database.

In accordance with another aspect of the present invention, an apparatusfor detecting a finger is provided. The apparatus includes an imagingsensor for capturing an image of a finger, and a computing system forgenerating a likelihood image of the finger from the captured image, forlocalizing the finger within the likelihood image, for determining aboundary of the finger, for determining a location of one or morecreases of the finger, and for comparing the determined location of theone or more creases with crease locations of a finger image stored in adatabase.

Other aspects, advantages, and salient features of the invention willbecome apparent to those skilled in the art from the following detaileddescription, which, taken in conjunction with the annexed drawings,discloses exemplary embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features, and advantages of certainexemplary embodiments of the present invention will be more apparentfrom the following description taken in conjunction with theaccompanying drawings, in which:

FIG. 1 illustrates a gesture based biometric system according to anexemplary embodiment of the present invention;

FIG. 2 illustrates basic modes of operating a biometric system accordingto an exemplary embodiment of the present invention;

FIG. 3 illustrates a system parameter learning mode according to anexemplary embodiment of the present invention;

FIG. 4 illustrates a finger enrollment mode according to an exemplaryembodiment of the present invention;

FIG. 5 illustrates a finger tracking mode according to an exemplaryembodiment of the present invention;

FIG. 6 illustrates steps of a detection algorithm for use in a biometricsystem according to an exemplary embodiment of the present invention;

FIGS. 7A-7C illustrate a coarse localization procedure according to anexemplary embodiment of the present invention;

FIG. 8 illustrates results of a Hough transform according to anexemplary embodiment of the present invention;

FIGS. 9A-9C illustrate images obtained during a finger boundarydetermination according to an exemplary embodiment of the presentinvention; and

FIG. 10 illustrates a projection profile of an extracted fingeraccording to an exemplary embodiment of the present invention.

Throughout the drawings, it should be noted that like reference numbersare used to depict the same or similar elements, features, andstructures.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The following description with reference to the accompanying drawings isprovided to assist in a comprehensive understanding of exemplaryembodiments of the invention as defined by the claims and theirequivalents. It includes various specific details to assist in thatunderstanding but these are to be regarded as merely exemplary.Accordingly, those of ordinary skill in the art will recognize thatvarious changes and modifications of the embodiments described hereincan be made without departing from the scope and spirit of theinvention. In addition, descriptions of well-known functions andconstructions are omitted for clarity and conciseness.

The terms and words used in the following description and claims are notlimited to the bibliographical meanings, but, are merely used by theinventor to enable a clear and consistent understanding of theinvention. Accordingly, it should be apparent to those skilled in theart that the following description of exemplary embodiments of thepresent invention are provided for illustration purpose only and not forthe purpose of limiting the invention as defined by the appended claimsand their equivalents.

It is to be understood that the singular forms “a,” “an,” and “the”include plural referents unless the context clearly dictates otherwise.Thus, for example, reference to “a component surface” includes referenceto one or more of such surfaces.

By the term “substantially” it is meant that the recited characteristic,parameter, or value need not be achieved exactly, but that deviations orvariations, including for example, tolerances, measurement error,measurement accuracy limitations and other factors known to those ofskill in the art, may occur in amounts that do not preclude the effectthe characteristic was intended to provide.

Exemplary embodiments of the present invention include an apparatus andmethod for implementing a biometric system using a low cost imagingsensor. An example of such a low cost imaging sensor is a cameraincluded in a currently available portable terminal. In an exemplaryimplementation, a feature of the biometric system includes recognitionof a gesture from a user and the performance of various functions basedon the recognized gesture. In an exemplary embodiment, the gesture is apointed finger oriented such that it occupies a large area of an imagecaptured by the imaging sensor.

A biometric system according to exemplary embodiments of the presentinvention may have numerous uses. For example, an image captured by thebiometric system may be used as an input to authenticate a user, toexecute a desired application, to control tasks of an executingapplication, and the like. As another example, an image captured by thebiometric system may be used as a control input, wherein the apparatuscan be used to accurately detect a position, size and orientation of thefinger in order to provide the user with a motion control mechanismhaving four degrees of freedom. As an example, such motion control canbe used to move objects in a scene rendered on a display of the portabledevice. As a means to execute a desired application, the apparatus canidentify the finger presented and can launch different applicationsbased on the identified finger. For example, the index finger may launchan email client whereas the ring finger may launch a gaming program.That is, different fingers may be mapped to different applications.Also, the apparatus can identify the finger presented among variousfingers enrolled in a database and can authenticate the captured imageas belonging to a user that is authorized to operate the system. Oncethe user is authenticated, the finger presented can then be used toperform additional functions, such as launching a program, controllingan input, and the like.

The following exemplary embodiments of the present invention aredescribed as applied to a “portable terminal.” However, it is to beunderstood that this is merely a generic term and that the invention isequally applicable to any of a mobile phone, a palm sized PersonalComputer (PC), a Personal Communication System (PCS), a Personal DigitalAssistant (PDA), a Hand-held PC (HPC), a smart phone, an InternationalMobile Telecommunication 2000 (IMT-2000) terminal, a wireless Local AreaNetwork (LAN) terminal, a laptop computer, a netbook, and the like.Accordingly, use of the term “portable terminal” should not be used tolimit application of the present inventive concepts to any certain typeof apparatus or device.

FIG. 1 illustrates a gesture based biometric system according to anexemplary embodiment of the present invention.

Referring to FIG. 1, the apparatus includes an imaging sensor 110 and acomputing system 120. The imaging sensor 110 is a low cost, lowresolution imaging sensor. An example of such a low cost imaging sensoris a camera that is currently available and typically integrated into aportable terminal or a web camera connected to a computer. Lowresolution implies small image sizes. In an exemplary implementation,the smallest resolution supported by the system is 240 pixels by 320pixels. The imaging sensor 110 may include a camera sensor (not shown)for capturing an image and converting an optical signal corresponding tothe captured image into an analog electrical signal. The imaging sensor110 may also include a signal processor (not shown) for converting theanalog electrical signal into digital data. The imaging sensor 110 maybe a Charge-Coupled Device (CCD) sensor or a ComplementaryMetal-Oxide-Semiconductor (CMOS) sensor, and the signal processor may bea Digital Signal Processor (DSP). The imaging sensor 110 and the signalprocessor may be realized as separate entities or as a single entity.During use, the imaging sensor 110 captures an image of a user's fingerand provides the image to the computing system 120.

The computing system 120 includes a logic unit 121 and an execution unit123. The logic unit 121 is provided for tracking, identifying andauthenticating the captured image received from the imaging sensor 110and for providing an output based on the captured image to the executionunit 123. The output provided by the logic unit 121 may include logicfor triggering a preferred action such as unlocking the portableterminal, launching different applications etc. The execution unit 123may be a general or specific control unit located within the portableterminal. The execution unit 123 receives logical input from the logicunit 121 and executes a function based on the received logic.

In the following description, the term “biometric system” denotes acombination of the imaging sensor and the programming logic fortracking, identification and authentication.

In an exemplary implementation, the biometric system of FIG. 1 may beincorporated into a laptop computer equipped with a web camera. That is,the laptop computer may include a logic unit 121 for tracking,identifying and authenticating a presented gesture as captured by theweb camera (i.e., imaging sensor 110) and for providing a logical outputto an execution unit 123 for executing a related function. Also, thelogic unit 121 can be a code running in the background, hardware logicspecialized for this function, or embedded logic in a digital signalprocessor. Similarly, the biometric system of FIG. 1 may be incorporatedinto a smart phone equipped with a camera. That is, the smart phone mayinclude a logic unit 121 for tracking, identifying and authenticating apresented gesture as captured by the phone's camera (i.e., imagingsensor 110) and for providing a logical output to an execution unit 123for executing a related function. The logic unit 121 can be a coderunning in the background, hardware logic specialized for this function,or embedded logic in a digital signal processor. Of course, these aremerely two examples illustrating potential implementation of theinvention and are not intended as limitations. Rather, as discussedabove, the present invention has applicability to a wide variety ofdevices.

In an exemplary embodiment, a button may be placed on the portableterminal that can be used to activate the system. It is noted howeverthat if the system were running unattended, it would consume significantbattery power. Thus, the system would be controlled to run unattendedonly if it were connected to a power source. Moreover, the system shouldstart working only when a response is expected from the user in the nextfew seconds in terms of presenting a finger. For example, when the cellphone rings, the imaging sensor should start capturing frames and try todetect the finger placed in front of it in order to detect a gesturecorresponding to a command to answer the phone, ignore the call, and thelike.

The following description will first illustrate modes of operation ofthe invention and will then illustrate more detail regarding means(i.e., algorithms, structures, etc.) to accomplish these modes. Theseparation of modes from means is provided for conciseness ofdescription. That is, while certain of the means have applicability to aplurality of the modes, only one description of each will be providedfor brevity.

FIG. 2 illustrates basic modes of operating a biometric system accordingto an exemplary embodiment of the present invention. FIG. 3 illustratesa system parameter learning mode according to an exemplary embodiment ofthe present invention. FIG. 4 illustrates a finger enrollment modeaccording to an exemplary embodiment of the present invention. FIG. 5illustrates a finger tracking mode according to an exemplary embodimentof the present invention.

Referring to FIG. 2, the basic operating modes of the biometric systeminclude a system parameter learning mode 210, a finger enrollment mode220, a finger authentication mode 230, and a finger tracking mode 240.Each of the operating modes are implemented as menu driven functionsthat include display screens for requesting user input, for displayingresults, and the like.

The system parameter learning mode 210 includes the capturing of animage using an imaging sensor of the biometric system. Morespecifically, the imaging sensor captures a sample of a finger regionand determines the color represented by the finger region. Asillustrated in FIG. 3, an exemplary system parameter learning mode 210involves an active user input, such as a menu driven process of theportable terminal, assisted by a rectangle 301 displayed on a screen ofthe portable terminal that is displaying frames streamed through theimaging sensor. In an exemplary implementation, the rectangle 301 has asize of 50 pixels×50 pixels and is displayed in the center of thescreen. As part of the active user input, the user is asked to placehis/her finger in front of the imaging sensor such that the rectangle301 is covered by the finger as shown in FIG. 3. The user can then pressa key on an input unit of the portable terminal to indicate that therectangular region is completely covered by the finger. This process canbe repeated a plurality of times (e.g., 20-30 times) under differentlighting conditions in order to capture a variety of skin tones.

In an exemplary embodiment, the system parameter learning mode 210 mayinvolve a machine learning technique such as a neural network, adecision tree based classifier, and a support vector machine basedclassifier, any of which can be used to determine the skin color from anobtained database of skin swatches. The skin swatches can also becaptured offline, without active user input, from different sourcesincluding digital pictures.

In the finger enrollment mode 220, a database of reference finger imagesis created using the biometric system. That is, in the finger enrollmentmode 220, the finger images of authorized users of the system areregistered in a database. In an exemplary implementation, the databaseis local to the portable terminal. However, the database may also beremotely accessed. In the finger enrolment mode 220, a user is requestedto present his/her finger in front of the imaging sensor. As illustratedin FIG. 4, the system locates the finger using a detection algorithm(explained below) and displays a rectangle 401 around the identifiedfinger region.

As also illustrated in FIG. 4, the creases 403 in the finger aredetected and used for aligning the finger to a desired frame ofreference. If the finger is correctly captured and the major creases ofthe finger are correctly marked by the biometric system, the user canenter a number from 0-9 indicating the specific finger presented. Forexample, the number 1 may be entered to indicate the ring finger of theleft hand. Of course, it is understood that this numbering system foreach of the ten fingers is merely an example and that the inventionenvisions many others. For example, the portable terminal may output amenu list including all possible fingers from which the user will selectthe appropriate finger that matches the captured image. The finger thuscaptured is stored along with its unique identification label in thelocal database.

In another exemplary embodiment, the biometric system locates the fingerusing a detection algorithm (explained below) that includes displayingthe rectangle 401 around the finger region. In an exemplaryimplementation, the biometric system also provides an identity of thefinger region (i.e., ring finger, left hand). The user is then calledupon to verify if the finger is correctly identified by the algorithm(i.e., correct identification of a ring finger). If the correct fingeris identified, the user is further requested to manually mark thecreases 403 in the finger that will be the discriminatory feature usedto identify the finger. The finger thus captured is stored along withits unique identification label in the local database. Alternatively,the biometric system may simply request the user to identify the fingerwithout executing a detection algorithm. For example, the biometricsystem may display a list of all possible finger types from which theuser will make a selection that matches the captured image. After theuser identifies the finger type, the user may then be requested tomanually mark the creases 403 of the identified finger. The capturedimage of the finger is stored in the database along with its uniqueidentification label.

In the finger authentication mode 230, users are authenticated bycomparing an image of a finger captured by an imaging sensor with theimages enrolled in the database. In a first step of the fingerauthentication mode 230, the biometric system attempts to identify anyfinger that is present in the image captured by the imaging sensor usinga detection algorithm (described below). The identified finger is thencompared with finger images stored in the local database and a matchscore is computed using a scoring algorithm (described below). Using thecomputed score, the biometric system outputs a decision which could beone of two: a “no finger detected” signal or the identity of thedetected finger.

In the finger tracking mode 240, the biometric system follows aprocedure to align the finger to a stored template. However, rather thanmatching the aligned finger segment to the one stored, the alignmentparameters (i.e., x-translation, y-translation, rotation, and scaling)are extracted from the system. These values can be used to translate,rotate, and scale an object displayed on the portable terminal such as aphotograph being displayed on the screen. Such values can also be usedto launch or otherwise control an application, and the like. FIG. 5illustrates a finger present in the image and the four degrees offreedom i.e. x-translation, y-translation, rotation and scalingextracted by aligning the finger with template crease pattern availablein the database.

The different modes of operating the biometric system as described aboveeach involve one or more components of a detection algorithm, exemplaryembodiments of which are described below. That is, as will be explainedin more detail below, a detection algorithm includes several componentsor steps, some of which are used in one or more of the operating modesof the biometric system and some of which are unique to a specific mode.

FIG. 6 illustrates steps of a detection algorithm for use in a biometricsystem according to an exemplary embodiment of the present invention.

Referring to FIG. 6, a detection algorithm includes an image capturestep 601, a likelihood image generation step 603, a coarse localizationstep 605, a fine localization step 607, a finger boundary basedlocalization step 609, a crease extraction step 611, a crease alignmentstep 613, and a matching step 615.

The image capture step 601 includes a well known function of a portableterminal in which a user captures an image. An image is typicallycaptured by means of selecting an image capture function of the portableterminal and selection of an image capturing button. As described abovewith reference to FIG. 1, a portable terminal includes an imaging sensor110 that may be used to capture an image.

The likelihood image generation of step 603 uses a color-likelihoodbased approach to detect the skin region. In an exemplaryimplementation, the process of the likelihood image generation involvesthe following steps. First, the skin-likeliness of each pixel of theimage captured by the imaging sensor is computed. This requires aprobability density function of skin color in a desirable color space.For this purpose, the hue and saturation components of the color areselected. It is noted that this probability density is required to becomputed only once during the system parameter learning mode 210 and canbe done either online using active user input or offline. Also, theprobability density function can be updated for different lightingconditions.

To compute the probability density of skin color, a set of sample skinpatches are collected in the system parameter learning mode 210, and allthe pixels from the available patches are pooled. The histogram of thislarge pool of pixels (i.e., the skin-color histogram) is used as anestimate for probability density of skin color. Given a pixel color, itsskin-likelihood is proportional to the value of the bin in theskin-color histogram associated with the pixel color as defined inEquation (1):L(x,y)=C(h(x,y),s(x,y))  (1)where, x and y are horizontal and vertical coordinates respectively ofthe pixel being considered, L is the likelihood, C is the skin colorhistogram, and h and s are the hue and saturation channels of thecaptured image, respectively.

In order to reduce the effect of noise pixels, the likelihood is set tozero if its value is smaller than a certain threshold as defined inEquation (2).

$\begin{matrix}{{L\left( {x,y} \right)} = {0\mspace{14mu}{\forall\left\{ \left( {x,y} \right) \middle| {{L\left( {x,y} \right)} < {\alpha\;{\max\limits_{({x,y})}{L\left( {x,y} \right)}}}} \right\}}}} & (2)\end{matrix}$

In an exemplary implementation, the obtained likelihood image is used tolocalize the finger using a three staged approach as described withreference to steps 605, 607, and 609. Once the skin likelihood image isobtained, the three stage approach is followed to localize the fingerregion.

In step 605, the first stage involves finger localization using imagemoments. Image moments have been extensively used and discussed inliterature as a method of identifying the location and orientation of anobject in an image. In an exemplary implementation, the image momentsare used to compute the centroid and orientation of the finger region.The centroid is given by Equation (3):

$\begin{matrix}{\left( {\overset{\_}{x},\overset{\_}{y}} \right) = \left( {{\frac{1}{m_{00}}{\sum\limits_{x,y}{{xL}\left( {x,y} \right)}}},{\frac{1}{m_{00}}{\sum\limits_{x,y}{{yL}\left( {x,y} \right)}}}} \right)} & (3)\end{matrix}$where L is the likelihood image and

$\begin{matrix}{m_{00} = {\sum\limits_{x,y}{L\left( {x,y} \right)}}} & (4)\end{matrix}$

The orientation of the finger is coarsely estimated using the imagemoments as defined by Equation (5):Θ=arctan(v _(y) /v _(x))  (5)where (v_(x),v_(y)) is the largest eigenvector of the image covariancematrix given by Equation (6):

$\begin{matrix}{{H = \begin{bmatrix}\mu_{20}^{\prime} & \mu_{11}^{\prime} \\\mu_{11}^{\prime} & \mu_{02}^{\prime}\end{bmatrix}}{where}} & (6) \\{\mu_{pq}^{\prime} = {{1/m_{00}}{\sum\limits_{x,y}{\left( {x - \overset{\_}{x}} \right)^{p}\left( {y - \overset{\_}{y}} \right)^{q}{L\left( {i,j} \right)}}}}} & (7)\end{matrix}$

FIGS. 7A-7C illustrate a coarse localization procedure according to anexemplary embodiment of the present invention.

Referring to FIGS. 7A-7C, a rectangular region centered at ( x, y) andoriented along Θ is cropped and used for further processing. Morespecifically, FIG. 7A illustrates a finger captured by an imagingsensor. Referring to FIG. 7B, a likelihood image L is illustrated afterits production in accordance with the above described equations, andFIG. 7C illustrates a finger region extracted using an image momentsbased approach.

In step 607, after image localization using moments, a Hough transformbased approach is used to further refine the segmentation. A Houghtransform in its simplest form is a linear transform for detectingstraight lines. As defined in Equation (8), a line can be represented bytwo parameters, the minimum distance of the line from an origin (ρ) andthe angle of the line from the origin with respect to the x-axis (θ).x cos(θ)+y sin(θ)=ρ  (8)

As can be discerned from Equation (8), fixing x and y provides asinusoid in the (ρ, θ) space. If there are multiple points lying on thesame line, say (ρ_(l), θ₁), their sinusoids will intersect at (ρ_(l),θ₁) in the (ρ, θ) space. And, if each point on the sinusoid casts onevote to the corresponding (ρ, θ)-bin, then the (ρ_(l), θ₁)-bin willreceive a significantly large number of votes as compared to itsneighbors and thus be easily detected. The basic Hough transformaccumulates these sinusoids corresponding to all the foreground pointsin the image.

For each foreground pixel in the image, a vote is cast for each of (ρ,θ) pairs such that the pixel lies on the line corresponding to (ρ, θ).Finally, top k (ρ, θ) pairs accumulating the highest votes areconsidered as the parameters for the detected lines. Because only onefinger is being recovered, only the (ρ, θ) pair corresponding to thehighest value of the accumulator matrix is considered.

In an alternative embodiment, a modified Hough Transform is used. Inthat exemplary embodiment, two different aspects are accommodated.First, use of a real valued image instead of a binary image is allowed.Second, thick lines are detected instead of 1-pixel wide lines. In orderto accommodate for the real valued image, all the non-zero values in theimage are considered for voting where each vote is weighted according tothe pixel value. To accommodate for the thick lines, given a pixel, inaddition to voting for the (ρ, θ) pair whose line crosses the pixel,votes are also considered for the (ρ−i, θ) pairs for i=−k, . . . , k forsome specified value of k. In effect, evidence for (ρ, θ) is alsoprovided by pixels lying on lines parallel to (ρ, θ) at a distance lessthan or equal to k. A line passing through the center of a thick ribbonis likely to accumulate more evidence than a line near the edge.

FIG. 8 illustrates results of a Hough transform according to anexemplary embodiment of the present invention.

Referring to FIG. 8, given the (ρ, θ) value provided by the Houghtransform, it is possible to extract the strip of image centered at line(ρ, θ) having a certain thickness from the original image. Morespecifically, FIG. 8 illustrates a rectangle extracted using imagemoments and the finger position and orientation as well as line 801detected by the Hough transform based approach.

Use of only the skin likelihood image may be considered for fingerlocalization. However, due to lighting variation, the likelihood may beunevenly distributed across the finger leading to slight deflection fromthe actual finger location. With step 609, this can be avoided byconsidering the actual image captured corresponding to the fingerlocation by detecting the finger boundary and using the detected fingerboundary for alignment. In step 609, the edge is extracted from the hueand intensity plane of the image separately using a Canny edge detectorand overlaid to obtain the final edge image. It is noted that the Cannyedge detector is merely an example and that any edge detection algorithmmay be used. Due to alignment based on a Hough transform, in theextracted image strip containing the finger, the line along the fingerdirection that cuts the strip into two equal halves is expected to beinside the finger region. With this assumption, from each point on thismid-line, consecutive pixels are checked in the vertically upward anddownward direction until an edge pixel is found. The two pixels detectedin the upward and downward direction are considered as the boundarypixels of the finger. By applying this procedure on all the pixels onthe mid-line, the top and bottom boundary of the finger is obtained. Themidpoint of the two boundary pixels for each point on the mid-line areobtained and are considered to constitute the skeleton of the finger.Assuming the origin to be at the center of the finger, the fingerskeleton is given by Equation (9):

$\begin{matrix}{{{skel}(x)} = \left\{ \begin{matrix}\frac{y_{top} + y_{bot}}{2} & \begin{matrix}{y_{top} = {\min\left( {{\left. y \middle| {E\left( {x,y} \right)} \right. = 1},{y > 0}} \right)}} \\{y_{bot} = {\max\left( {{\left. y \middle| {E\left( {x,y} \right)} \right. = 1},{y < 0}} \right)}}\end{matrix} \\\phi & {ow}\end{matrix} \right.} & (9)\end{matrix}$where E(x,y) has a value 1 if (x,y) is an edge pixel.

FIGS. 9A-9C illustrate images obtained during a finger boundarydetermination according to an exemplary embodiment of the presentinvention.

Referring to FIGS. 9A-9C, in order to eliminate the effect of extraneousobjects in the extracted strip, portions of the rectangular strip oneach side along its length are discarded. In an exemplaryimplementation, the discarded length is equal to 20% of the totallength. Then, starting from the center, the skeleton is traced on eitherside horizontally until there is a discontinuity larger than a certainthreshold. If the length of the continuous regions on either side of thecenter is greater than the certain threshold, the skeleton is consideredvalid and otherwise the edge based localization is discarded as it mayactually degrade the localization provided by the Hough transform. Theposition and orientation of the skeleton is recovered by fitting a lineto the skeleton in the least square sense. The extracted finger image isthen again aligned along the fitted line. FIGS. 9A-9C respectivelyillustrate a finger strip extracted based on a Hough transform, theskeleton of the finger strip, and the finger aligned using the skeleton.It is noted that since the algorithm described does not differentiatebetween the finger itself and its 180° rotated version, both the fingersegment and its 180° rotated version are used for matching with thestored template.

There are several alternative exemplary embodiments to those describedabove. For example, the modified Hough transform based technique may bedirectly used on the captured image without considering the imagemoments based finger localization. Such an approach will produce betterfinger localization. However, its computational cost would be high. Inanother alternative, edge based localization may be directly appliedafter the image moment based localization. This technique can benoticeably faster than the case when modified Hough transform is appliedbut it may lead to reduction in accuracy. As another alternative, theHough transform can be replaced by a probabilistic Hough transform.However, while this replacement would also lead to noticeableimprovement in computational efficiency, it may also reduce the accuracyof the system.

In another exemplary embodiment, the edge based alignment may beeliminated and the finger region provided by the modified Houghtransform can be directly used as the final finger segment. This examplecan be utilized if the user is habituated and always provides the fingerat a certain distance from the imaging sensor and the ‘k’ parameter ofthe modified Hough transform is appropriately tuned.

In yet another exemplary embodiment, a sophisticated image segmentationprocedure such as mean-shift or normalized cut can be applied in orderto obtain the finger segment and thus the finger edge. However, whilethis may provide better accuracy, the system thus developed would havehigh computational complexity.

In another exemplary embodiment, the boundaries are extracted from theobjects detected in the captured image using the image segmentationtechniques. In the finger enrollment mode, the system displays theboundary of each segment one at a time overlaid on the original image tothe user and asks the user if the segment corresponds to the finger. Theuser can indicate the segment to be of the finger or not by pressing anappropriate key. If a segment is indicated by the user to belong to thefinger, its boundary is stored in the system along with the image colorvalues corresponding to the pixels inside the segment. Duringauthentication, a segmentation procedure is again used in order toidentify the various segments and the boundaries corresponding to eachof these segments is matched with that corresponding to the fingerstored in the system using a trimmed, iterative closest point matchingtechnique. The alignment parameters thus obtained are then used to alignthe captured image to the template. A common rectangular region insideeach finger segment is extracted and is matched using the proceduredescribed below.

In step 611, finger creases are extracted for use in aligning the fingerwith the stored template. Creases on fingers are generally perpendicularto the length of the finger and are generally darker than thesurrounding region. In order to extract the creases, a projectionprofile of the finger is first obtained using Equation (10):

$\begin{matrix}{{{Proj}(x)} = {\sum\limits_{y}{I\left( {x,y} \right)}}} & (10)\end{matrix}$where I is the grayscale image strip extracted using the skeleton basedalignment.

FIG. 10 illustrates a projection profile of an extracted fingeraccording to an exemplary embodiment of the present invention.

Referring to FIG. 10, it is observed that the troughs in the projectionprofile correspond to the finger creases. In an exemplaryimplementation, these troughs are obtained from the projection profileas follows. First, for each point, ‘a’, on the x-axis, a segment of apredefined length, such as ‘b’ pixels, centered at ‘a’, is considered.The area of a projection graph below a certain threshold in the segmentis considered as the crease indicator (C_(ind)) value at point ‘a’ givenby Equations (11) and (12).

$\begin{matrix}{{C_{ind}(x)} = {\sum\limits_{k = {{- b}/2}}^{b/2}{f\left( {{{th}_{proj}(x)} - {{Proj}\left( {x + k} \right)}} \right)}}} & (11) \\{{{{th}_{proj}(x)} = {\min\left( {{{Proj}\left( {x - {b/2}} \right)},{{Proj}\left( {x + {b/2}} \right)}} \right)}},{{f(a)} = \left\{ \begin{matrix}0 & {{{if}\mspace{14mu} a} < 0} \\a & {{{if}\mspace{14mu} a} > 0}\end{matrix} \right.}} & (12)\end{matrix}$

FIG. 10 also illustrates the projection profile and the crease indicatorfunction computed from the projection profile.

Next, peaks are identified in the crease indicator function using asimple procedure which checks if the first non-equal values on the leftand right of a point are smaller than the value itself. If there are twopeaks sufficiently close to each other, then only one of them isconsidered. Given the set of all the peaks identified, the top sevenpeaks having the highest value for C_(ind) are considered for furtherprocessing. Notably, a finger usually has four major creases and thechoice of the number of creases selected is governed by the expectedamount of spurious creases detected in the finger with a high C_(ind)value.

In step 613, each pair of creases in the captured finger is aligned witheach pair of the stored template. For each such alignment, whichinvolves scaling and translation of the finger, Equation (13) is used tocompute the fitness score of the alignment:

$\begin{matrix}{\mspace{79mu}{{{{fitness}(i)} = {\sum\limits_{i}{\left( {{th} - {d\left( c_{i}^{q} \right)}} \right){w\left( c_{i}^{q} \right)}}}}\mspace{79mu}{{d\left( c_{i}^{q} \right)} = \left\{ {{{\begin{matrix}{\min\limits_{j}\left( {{c_{i}^{q} - c_{j}^{t}}} \right)} & {{{if}\mspace{14mu}{\min\limits_{j}\left( {{c_{i}^{q} - c_{i}^{t}}} \right)}} \leq {th}} \\{th} & {ow}\end{matrix}{w\left( c_{i}^{q} \right)}} = \frac{{{Cind}\left( c_{i}^{q} \right)} + {{Cind}\left( c_{j}^{t} \right)}}{{{{{Cind}\left( c_{i}^{q} \right)} - {{Cind}\left( c_{j}^{q} \right)}}} + \frac{{\max\left( {{Cind}\left( c_{k}^{q} \right)} \right)} + {\max\left( {{Cind}\left( c_{k}^{t} \right)} \right)}}{10}}},\mspace{79mu}{j = {\arg\;{\min\left( {{c_{i}^{q} - c_{j}^{t}}} \right)}}}} \right.}}} & (13)\end{matrix}$where c_(i) ^(q) and c_(i) ^(t) are crease locations after crease pairbased alignment in the captured and the stored finger, respectively,that do not belong to the pair of creases being aligned.

The corresponding pair obtaining the greatest fitness score isconsidered for final alignment. The query finger is then scaled andtranslated to align with the enrolled finger. A crease from the capturedfinger is said to be corresponding to a crease from an enrolled fingerif the distance between the two creases after alignment is smaller thana threshold.

In step 615, given the enrolled fingers, the local regions extractednear the corresponding creases are matched. To match two local regions,the Red, Green, Blue (RGB) planes of the finger image strip areconverted into grayscale and normalized. A normalized finger image(I_(norm)) may be determined using Equation (14).

$\begin{matrix}{{I_{norm}\left( {x,y} \right)} = \left\{ {{\begin{matrix}{{{Id}\left( {{I\left( {i,j} \right)} > 0} \right)}\left( {M + \sqrt{\frac{v_{req}}{v\left( {x,y} \right)}\begin{pmatrix}{{I\left( {x,y} \right)} -} \\{m\left( {x,y} \right)}\end{pmatrix}^{2}}} \right)} & {{{if}\mspace{14mu}{I\left( {x,y} \right)}} > {m\left( {x,y} \right)}} \\{{{Id}\left( {{I\left( {i,j} \right)} > 0} \right)}\left( {M - \sqrt{\frac{v_{req}}{v\left( {x,y} \right)}\begin{pmatrix}{{I\left( {x,y} \right)} -} \\{m\left( {x,y} \right)}\end{pmatrix}^{2}}} \right)} & {ow}\end{matrix}\mspace{79mu}{m\left( {x,y} \right)}} = {{\frac{\sum\limits_{i,{j = {({{x - t},{y - t}})}}}^{{x + t},{y + t}}{{I\left( {i,j} \right)}{{Id}\left( {{I\left( {i,j} \right)} > 0} \right)}}}{\sum\limits_{i,{j = {({{x - t},{y - t}})}}}^{{x + t},{y + t}}{{Id}\left( {{I\left( {i,j} \right)} > 0} \right)}}{v\left( {x,y} \right)}} = {{\frac{\sum\limits_{i,{j = {({{x - t},{y - t}})}}}^{{x + t},{y + t}}{\left( {{I\left( {i,j} \right)} - {m\left( {x,y} \right)}} \right)^{2}{{Id}\left( {{I\left( {i,j} \right)} > 0} \right)}}}{\sum\limits_{i,{j = {({{x - t},{y - t}})}}}^{{x + t},{y + t}}{{Id}\left( {{I\left( {i,j} \right)} > 0} \right)}}\mspace{79mu}{{Id}(a)}} = \left\{ \begin{matrix}1 & {a = {true}} \\0 & {a = {false}}\end{matrix} \right.}}} \right.} & (14)\end{matrix}$

To determine a match score, it is assumed that S1 and S2 are two localregions to be matched. With that assumption, the match score may bedetermined using Equation (15).

$\begin{matrix}{{{{match}\left( {{S\; 1},{S\; 2}} \right)} = {\max\limits_{t = {- {\gamma:{2:\gamma}}}}\left( {{dot}\left( {{S\; 1},{{clip}\left( {{vshift}\left( {{S\; 2},t} \right)} \right)},{S\; 1}} \right)} \right)}}{{{vshift}\left( {R,t} \right)} = \left\{ {{S❘{S\left( {x,y} \right)}} = {R\left( {x,{y + t}} \right)}} \right\}}{{{clip}\left( {R,S} \right)} = \left\{ {{U❘{U\left( {x,y} \right)}} = {{\begin{Bmatrix}{R\left( {x,y} \right)} & {{{if}\mspace{14mu}{S\left( {x,y} \right)}} > 0} \\0 & {ow}\end{Bmatrix}{{dot}\left( {R,S} \right)}} = \frac{\sum\limits_{x,y}{\left( {255 - {R\left( {x,y} \right)}} \right)\left( {255 - {S\left( {x,y} \right)}} \right)}}{\sqrt{\sum\limits_{x,y}\left( {255 - {S\left( {x,y} \right)}} \right)^{2}}\sqrt{\sum\limits_{x,y}\left( {255 - {R\left( {x,y} \right)}} \right)^{2}}}}} \right.}} & (15)\end{matrix}$

The final match score is computed as average of the individual matchscores of the different local regions associated with the correspondingcreases.

In another exemplary embodiment, the image normalization procedure canbe replaced by another procedure such as histogram equalization.Furthermore, the image segment matching procedure can be replaced by amore sophisticated matching technique such as mutual information orother machine learning techniques. For example, the dot(R,S) function inEquation (15) can be replaced by dotM(R-S) which classifies (R-S) as amatch or no match using techniques such as support vector machines,decision tree classifier or neural networks.

As described above, exemplary embodiments of the present invention haveapplicability in a wide variety of applications. For example, the systemmay be used for motion control. In such an exemplary implementation, thesystem follows the usual procedure to align the finger to the storedtemplate. However, instead of simply matching the aligned finger segmentto the stored template, the alignment parameters of the finger segment,in terms of x-translation, y-translation, rotation, and scaling, arecompared to those of the stored template and used to control themovement of a mouse pointer. In an exemplary implementation, the mousepointer can be a 3D pointer in case of 3D scene navigation wherein thez-translation is provided by the scaling component.

Still further, an exemplary implementation of the present invention isrelated to mouse tracking. In such an embodiment, only the first frameis processed using the proposed approach to align the finger. The fingeris tracked in the subsequent frames using the commonly used objecttracking techniques such as Kanade-Lucas-Tomasi (KLT) Tracker, meanshift tracker, etc. If the tracking algorithm requires a certain set ofpoints on the image to be tracked, the points can be randomly sampledinside the small segments associated with each crease.

In an exemplary implementation, during the image capture step, a displayscreen present on the same side of the phone as the imaging sensor maybe lit with a specific color. The skin-likelihood histogram is computedseparately for each such illumination and during authentication the samecolor is lit on the display.

In another exemplary embodiment of the current invention, averagebrightness of the image is computed first and if its value is below acertain threshold, the system will indicate to the user, using an audioor a visual cue, that the system cannot perform correctly under thecurrent environmental conditions and thus the traditional modes ofinteraction with the cell phone must be used. As an alternative, insteadof computing the average brightness, this information can be accessedfrom other devices having the same capabilities present in the vicinitythrough a Bluetooth or other source of communication between the devicesavailable.

In another exemplary embodiment of the current invention, if the fingeridentification is successful with significant reliability, smallsegments of the color image are extracted around the matched creases andused to update the skin color histogram. In order to update thehistogram, a new histogram is generated using the small segments and aweighted sum of previous and the current histogram is now used as thenew histogram. The weight can be computed using various heuristictechniques.

In another exemplary embodiment of the current invention, the other sideof the finger is used for authentication. This would include knuckledetection and nail shape recognition. However, matching these featuresmight require very different set of features.

An advantage of the proposed system is its relaxed requirement of userattention which significantly adds to the convenience of using thesystem. For example, a user receiving a call while driving can simplyshow a specific finger to the cell phone to either ignore or respond tothe call without having to devote significant attention to the cellphone. This is important especially when a significant proportion ofdriving accidents can be linked to diversion of attention towards cellphones. Further, such application is also useful for those havinglimited sight. In that case, the user would not have to find and pressspecific buttons on the portable terminal in order to operate afunction. In general, a portable terminal will become less intrusive andthus more convenient.

While the invention has been shown and described with reference tocertain exemplary embodiments thereof, it will be understood by thoseskilled in the art that various changes in form and details may be madetherein without departing from the spirit and scope of the invention asdefined by the appended claims and their equivalents.

What is claimed is:
 1. A method of a portable terminal for detecting afinger, the method comprising: capturing an image of a finger;generating a likelihood image of the finger from the captured image;localizing the finger within the likelihood image by performing a coarselocalization using image moments and performing a fine localizationusing a Hough transform; determining a boundary of the finger;determining a location of one or more creases of the finger; andcomparing the determined location of the one or more creases with creaselocations of a finger image stored in a database.
 2. The method of claim1, wherein the generating of the likelihood image of the finger from thecaptured image comprises using a color-likelihood algorithm to detect askin region of the finger.
 3. The method of claim 2, wherein the usingof the color-likelihood algorithm comprises using the followingequation:L(x,y)=C(h(x,y),s(x,y)) where, x and y are coordinates of a pixel of thecaptured image, L is the likelihood, C is a skin color histogram, h(.,.)is a hue of the captured image, and s(.,.) is a saturation channel ofthe captured image.
 4. The method of claim 3, wherein the likelihoodvalue is set to zero if a value computed using the equation is less thana threshold.
 5. The method of claim 3, wherein the performing of thecoarse localization comprises determining a centroid and orientation ofthe finger using the following equations:$\left( {\overset{\_}{x},\overset{\_}{y}} \right) = \left( {{\frac{1}{m_{00}}{\sum\limits_{x,y}{{xL}\left( {x,y} \right)}}},{\frac{1}{m_{00}}{\sum\limits_{x,y}{{yL}\left( {x,y} \right)}}}} \right)$where L is the likelihood image and${m_{00} = {\sum\limits_{x,y}{L\left( {x,y} \right)}}};$ andΘ=arctan(v _(y) /v _(x)) where (v_(x), v_(y)) is the largest eigenvectorof the image covariance matrix given by $H = \begin{bmatrix}\mu_{20}^{\prime} & \mu_{11}^{\prime} \\\mu_{11}^{\prime} & \mu_{02}^{\prime}\end{bmatrix}$ where$\mu_{pq}^{\prime} = {{1/m_{00}}{\sum\limits_{x,y}{\left( {x - \overset{\_}{x}} \right)^{p}\left( {y - \overset{\_}{y}} \right)^{q}{{L\left( {x,y} \right)}.}}}}$6. The method of claim 5, wherein the determining of the boundary of thefinger comprises determining a skeleton of the finger using thefollowing equation: ${{skel}(x)} = \left\{ \begin{matrix}\frac{y_{top} + y_{bot}}{2} & \begin{matrix}{y_{top} = {\min\left( {{{y❘{E\left( {x,y} \right)}} = 1},{y > 0}} \right)}} \\{y_{bot} = {\max\left( {{{y❘{E\left( {x,y} \right)}} = 1},{y < 0}} \right)}}\end{matrix} \\\phi & {ow}\end{matrix} \right.$ where E is the edge image.
 7. The method of claim6, wherein the determining of the location of the one or more creases ofthe finger comprises determining a projection profile of the fingerusing the following equation:${{Proj}(x)} = {\sum\limits_{y}{I\left( {x,y} \right)}}$ where I is agrayscale image strip extracted using the skeleton.
 8. The method ofclaim 7, wherein the determining of the location of the one or morecreases of the finger further comprises determining troughs in theprojection profile using the following equations:${{C_{ind}(x)} = {\sum\limits_{k = {{- b}/2}}^{b/2}{f\left( {{{th}_{proj}(x)} - {{Proj}\left( {x + k} \right)}} \right)}}};$${f(a)} = \left\{ {{\begin{matrix}0 & {{{if}\mspace{14mu} a} < 0} \\a & {{{if}\mspace{14mu} a} > 0}\end{matrix};{{{and}{{th}_{proj}(x)}} = {\min\left( {{{Proj}\left( {x - {b/2}} \right)},{{Proj}\left( {x + {b/2}} \right)}} \right)}}},} \right.$where a is a point along an x-axis of the projection profile, b is thesize of segment of pixels of a predefined length centered at a, andth_(proj) is a threshold of the segment.
 9. The method of claim 8,wherein the comparing of the determined location of the one or morecreases with the crease locations of the finger image stored in thedatabase comprises determining an alignment fitness using the followingequations:$\mspace{20mu}{{{fitness}(i)} = {\sum\limits_{i}{\left( {{th} - {d\left( c_{i}^{q} \right)}} \right){w\left( c_{i}^{q} \right)}}}}$$\mspace{20mu}{{d\left( c_{i}^{q} \right)} = \left\{ {{{\begin{matrix}{\min\limits_{j}\left( {{c_{i}^{q} - c_{j}^{t}}} \right)} & {{{if}\mspace{14mu}{\min\limits_{j}\left( {{c_{i}^{q} - c_{i}^{t}}} \right)}} \leq {th}} \\{th} & {ow}\end{matrix}{w\left( c_{i}^{q} \right)}} = \frac{{{Cind}\left( c_{i}^{q} \right)} + {{Cind}\left( c_{j}^{t} \right)}}{{{{{Cind}\left( c_{i}^{q} \right)} - {{Cind}\left( c_{j}^{q} \right)}}} + \frac{{\max\left( {{Cind}\left( c_{k}^{q} \right)} \right)} + {\max\left( {{Cind}\left( c_{k}^{t} \right)} \right)}}{10}}},\mspace{20mu}{j = {{argmin}\left( {{c_{i}^{q} - c_{j}^{t}}} \right)}}} \right.}$where c_(i) ^(q) and c_(i) ^(t) are crease locations after crease pairbased alignment in the captured and the stored finger, respectively,that do not belong to the pair of creases being aligned.
 10. The methodof claim 1, wherein the performing of the fine localization comprisesevaluating non-zero pixels of a binary image to detect a line having awidth of one pixel using the following equation:x cos(θ)+y sin(θ)=ρ where (ρ) is a minimum distance of the line from anorigin and θ is an angle of the line from an origin with respect to anx-axis, wherein values of pairs of ρ and θ are determined for eachpixel, the determined values of pairs of ρ and θ are accumulated, andthe highest accumulated values of pairs of ρ and θ are considered as theline.
 11. The method of claim 1, wherein the performing of the finelocalization comprises evaluating non-zero pixels of a real image todetect a line having a width of 2k+1 pixels using the followingequation:x cos(θ)+y sin(θ)=ρ where (ρ) is a minimum distance of the line from anorigin and θ is an angle of the line from an origin with respect to anx-axis, wherein values of pairs of ρ and θ are determined for eachpixel, values of pairs of ρ−i and θ are determined for each pixel fori=−k . . . k, the determined values of pairs of ρ and θ and ρ−i and θare accumulated, the values of pairs of ρ and θ and ρ−i and θ areweighted according to a pixel value, and the highest accumulated valuesof pairs of ρ and θ and ρ−i and θ are considered as the line.
 12. Themethod of claim 1, further comprising: determining an alignmentparameter of the captured image; comparing the alignment parameter ofthe captured image with an alignment parameter of the stored fingerimage; and controlling a function of the portable terminal based on adifference between the alignment parameter of the captured image and thealignment parameter of the stored finger image.
 13. The method of claim12, wherein the alignment parameter of the captured image comprises atleast one of an x-translation, a y-translation, a rotation, and ascaling.
 14. The method of claim 1, wherein the capturing the image ofthe finger comprises: capturing the image of the finger using an imagingsensor; and displaying a screen that is sequentially lit with aplurality of specific colors on a display unit that is on a same side ofthe portable terminal as the imaging sensor, wherein, the generating ofthe likelihood image comprises generating separate likelihood images foreach of the plurality of specific colors sequentially lit on the displayunit.
 15. The method of claim 1, further comprising: determining anaverage brightness of the image of the finger; determining if theaverage brightness is below a threshold; and if the determined averagebrightness is below the threshold, providing an indication that a fingergesture detection is unavailable.
 16. The method of claim 1, furthercomprising: controlling a function of the portable terminal based on thecomparison of the determined location of the one or more creases of thecaptured finger with the crease locations of the finger image stored ina database.
 17. An apparatus for detecting a finger, the apparatuscomprising: an imaging sensor for capturing an image of a finger; and acomputing system for generating a likelihood image of the finger fromthe captured image, for localizing the finger within the likelihoodimage by preforming a coarse localization using image moments andperforming a fine localization using a Hough transform, for determininga boundary of the finger, for determining a location of one or morecreases of the finger, and for comparing the determined location of theone or more creases with crease locations of a finger image stored in adatabase.
 18. The apparatus of claim 17, wherein the computing systemgenerates the likelihood image of the finger from the captured image byusing a color-likelihood algorithm to detect a skin region of thefinger.
 19. The apparatus of claim 18, wherein the using of thecolor-likelihood algorithm comprises using the following equation:L(x,y)=C(h(x,y),s(x,y)) where, x and y are coordinates of a pixel of thecaptured image, L is the likelihood, C is a skin color histogram, h is ahue of the captured image, and s is a saturation channel of the capturedimage.
 20. The apparatus of claim 19, wherein the likelihood value isset to zero if a value computed using the equation is less than athreshold.
 21. The apparatus of claim 19, wherein the computing systemperforms the coarse localization by determining a centroid andorientation of the finger using the following equations:$\left( {\overset{\_}{x},\overset{\_}{y}} \right) = \left( {{\frac{1}{m_{00}}{\sum\limits_{x,y}{{xL}\left( {x,y} \right)}}},{\frac{1}{m_{00}}{\sum\limits_{x,y}{{yL}\left( {x,y} \right)}}}} \right)$where L is the likelihood image and${m_{00} = {\sum\limits_{x,y}{L\left( {x,y} \right)}}};$ andΘ=arctan(v _(y) /v _(x)) where (v_(x),v_(y)) is the largest eigenvectorof the image covariance matrix given by $H = \begin{bmatrix}\mu_{20}^{\prime} & \mu_{11}^{\prime} \\\mu_{11}^{\prime} & \mu_{02}^{\prime}\end{bmatrix}$ where$\mu_{pq}^{\prime} = {{1/m_{00}}{\sum\limits_{x,y}{\left( {x - \overset{\_}{x}} \right)^{p}\left( {y - \overset{\_}{y}} \right)^{q}{{L\left( {x,y} \right)}.}}}}$22. The apparatus of claim 21, wherein the computing system determinesthe boundary of the finger by determining a skeleton of the finger usingthe following equation: ${{skel}(x)} = \left\{ \begin{matrix}\frac{y_{top} + y_{bot}}{2} & \begin{matrix}{y_{top} = {\min\left( {{{y❘{E\left( {x,y} \right)}} = 1},{y > 0}} \right)}} \\{y_{bot} = {\max\left( {{{y❘{E\left( {x,y} \right)}} = 1},{y < 0}} \right)}}\end{matrix} \\\phi & {ow}\end{matrix} \right.$ where E is the edge image.
 23. The apparatus ofclaim 11, wherein the computing system determines the location of theone or more creases of the finger by determining a projection profile ofthe finger using the following equation:${{Proj}(x)} = {\sum\limits_{y}{I\left( {x,y} \right)}}$ where I is agrayscale image strip extracted using the skeleton.
 24. The apparatus ofclaim 23, wherein the computing system determines the location of theone or more creases of the finger further by determining troughs in theprojection profile using the following equations:${{C_{ind}(x)} = {\sum\limits_{k = {{- b}/2}}^{b/2}{f\left( {{{th}_{proj}(x)} - {{Proj}\left( {x + k} \right)}} \right)}}};$${f(a)} = \left\{ {{\begin{matrix}0 & {{{if}\mspace{14mu} a} < 0} \\a & {{{if}\mspace{14mu} a} > 0}\end{matrix};{{{and}{{th}_{proj}(x)}} = {\min\left( {{{Proj}\left( {x - {b/2}} \right)},{{Proj}\left( {x + {b/2}} \right)}} \right)}}},} \right.$where a is a point along an x-axis of the projection profile, b is thelength of a segment of pixels of a predefined length centered at a, andthproj is a threshold of the segment.
 25. The apparatus of claim 24,wherein the computing system compares the determined location of the oneor more creases with the crease locations of the finger image stored inthe database by determining an alignment fitness using the followingequations:$\mspace{20mu}{{{fitness}(i)} = {\sum\limits_{i}{\left( {{th} - {d\left( c_{i}^{q} \right)}} \right){w\left( c_{i}^{q} \right)}}}}$$\mspace{20mu}{{d\left( c_{i}^{q} \right)} = \left\{ {{{\begin{matrix}{\min\limits_{j}\left( {{c_{i}^{q} - c_{j}^{t}}} \right)} & {{{if}\mspace{14mu}{\min\limits_{j}\left( {{c_{i}^{q} - c_{i}^{t}}} \right)}} \leq {th}} \\{th} & {ow}\end{matrix}w\left( c_{i}^{q} \right)} = \frac{{{Cind}\left( c_{i}^{q} \right)} + {{Cind}\left( c_{j}^{t} \right)}}{{{{{Cind}\left( c_{i}^{q} \right)} - {{Cind}\left( c_{j}^{q} \right)}}} + \frac{{\max\left( {{Cind}\left( c_{k}^{q} \right)} \right)} + {\max\left( {{Cind}\left( c_{k}^{t} \right)} \right)}}{10}}},\mspace{20mu}{j = {{argmin}\left( {{c_{i}^{q} - c_{j}^{t}}} \right)}}} \right.}$where and c_(i) ^(q) and c_(i) ^(t) are crease locations after creasepair based alignment in the captured and the stored finger,respectively, that do not belong to the pair of creases being aligned.26. The apparatus of claim 17, wherein the computing system performs thefine localization by evaluating non-zero pixels of a binary image todetect a line having a width of one pixel using the following equation:x cos(θ)+y sin(θ)=ρ where (ρ) is a minimum distance of the line from anorigin and θ is an angle of the line from an origin with respect to anx-axis, wherein values of pairs of ρ and θ are determined for eachpixel, the determined values of pairs of ρ and θ are accumulated, andthe highest accumulated values of pairs of ρ and θ are considered as theline.
 27. The apparatus of claim 17, wherein the computing systemperforms the fine localization by evaluating non-zero pixels of a realimage to detect a line having a width of 2k+1 pixels using the followingequation:x cos(θ)+y sin(θ)=ρ where (ρ) is a minimum distance of the line from anorigin and θ is an angle of the line from an origin with respect to anx-axis, wherein values of pairs of ρ and θ are determined for eachpixel, values of pairs of ρ−i and θ are determined for each pixel fori=−k . . . k, the determined values of pairs of ρ and θ and ρ−i and θare accumulated, the values of pairs of ρ and θ and ρ−i and θ areweighted according to a pixel value, and the highest accumulated valuesof pairs of ρ and θ and ρ−i and θ are considered as the line.
 28. Theapparatus of claim 17, wherein the computing system determines analignment parameter of the captured image, compares the alignmentparameter of the captured image with an alignment parameter of thestored finger image, and controls a function of the portable terminalbased on a difference between the alignment parameter of the capturedimage and the alignment parameter of the stored finger image.
 29. Theapparatus of claim 28, wherein the alignment parameter of the capturedimage comprises at least one of an x-translation, a y-translation, arotation, and a scaling.
 30. The apparatus of claim 17, furthercomprising: a display unit for displaying a screen that is sequentiallylit with a plurality of specific colors, the display unit located on asame side of the apparatus as the imaging sensor, wherein, the computingsystem generates the likelihood image by generating separate likelihoodimages for each of the plurality of specific colors sequentially lit onthe display unit.
 31. The apparatus of claim 17, wherein the computingsystem determines an average brightness of the image of the finger,determines if the average brightness is below a threshold, and, if thedetermined average brightness is below the threshold, provides anindication that a finger gesture detection is unavailable.
 32. Theapparatus of claim 17, wherein the computing system controls a functionbased on the comparison of the determined location of the one or morecreases of the captured finger with the crease locations of the fingerimage stored in a database.